Existing data analysis techniques typically represent data in a 2-dimensional space, even if the data is complex and represents many different sets of possibly related information. For example, to analyze consumer purchasing volume for purposes of making predictions/product recommendations, existing techniques typically treat factors that affect purchasing behavior independently and separately analyze pairs of relationships. The analysis is typically done using matrices. For example, data such as inflation rate—purchasing volume, consumer price index—purchasing volume, and exchange rate—purchasing volume can be used to form matrices which are analyzed to determine how factors such as inflation rate, consumer price index, and exchange rate individually affect purchasing volume. Because the analysis treats separate factors independently, the results are often limited in terms of providing insight into complex relationships among the data and how different factors may influence each other.
Attempts to analyze data in multiple dimensions have been made but many existing techniques incorrectly interpret the relationships of multidimensional data and therefore often lead to inaccurate and/or inconsistent results. For example, incorrect analysis would lead to an inaccurate data model, which in turn leads to inconsistent predictions.
Furthermore, existing analysis techniques are often computationally intensive and require large amounts of memory and/or storage space for large data sets.
It would therefore be useful to have a way of analyzing multidimensional data that produces accurate and consistent results, that is computationally efficient, and that saves memory and/or storage space.